Literature DB >> 23599765

Abnormal methylation of seven genes and their associations with clinical characteristics in early stage non-small cell lung cancer.

Yangxing Zhao1, Huafu Zhou, Kelong Ma, Jinfeng Sun, Xu Feng, Junfeng Geng, Jun Gu, Wei Wang, Hongyu Zhang, Yinghua He, Shicheng Guo, Xiaoyu Zhou, Jian Yu, Qiang Lin.   

Abstract

To identify novel abnormally methylated genes in early stage non-small cell lung cancer (NSCLC), we analyzed the methylation status of 13 genes (ALX1, BCL2, FOXL2, HPP1, MYF6, OC2, PDGFRA, PHOX2A, PITX2, RARB, SIX6, SMPD3 and SOX1) in cancer tissues from 101 cases of stage I NSCLC patients and lung tissues from 30 cases of non-cancerous lung disease controls, using methylation-specific PCR (MSP). The methylation frequencies (29.70-64.36%) of 7 genes (MYF6, SIX6, SOX1, RARB, BCL2, PHOX2A and FOLX2) in stage I NSCLC were significantly higher compared with those in non-cancerous lung disease controls (P<0.05). The co-methylation of SIX6 and SOX1, or the co-methyaltion of SIX6, RARB and SOX1 was associated with adenosquamous carcinoma (ADC), and the co-methylation of BCL2, RARB and SIX6 was associated with smoking. A panel of 4 genes (MYF6, SIX6, BCL2 and RARB) may offer a sensitivity of 93.07% and a specificity of 83.33% in the diagnosis of stage I NSCLC. Furthermore, we also detected the expression of 8 pathological markers (VEGF, HER-2, P53, P21, EGFR, CHGA, SYN and EMA) in cancer tissues of stage I NSCLC by immunohistochemistry, and found that high expression levels of p53 and CHGA were associated with the methylation of BCL2 (P=0.025) and PHOX2A (P=0.023), respectively. In this study, among the 7 genes which demonstrated hypermethylation in stage I NSCLC compared with non-cancerous lung diseases, 5 genes (MYF6, SIX6, PHOX2A, FOLX2 and SOX1) were found for the first time to be abonormally methylated in NSCLC. Further study of these genes shed light on the carcinogenesis of NSCLC.

Entities:  

Keywords:  DNA methylation; non-small cell lung cancer; smoking; stage I

Year:  2013        PMID: 23599765      PMCID: PMC3629069          DOI: 10.3892/ol.2013.1161

Source DB:  PubMed          Journal:  Oncol Lett        ISSN: 1792-1074            Impact factor:   2.967


Introduction

In China, the incidence of lung cancer is increasing every year, and the mortality rate of lung cancer has risen to the highest among all types of cancer (1). The main type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases. Abnormalities in epigenetics are known to play important roles in the development and progression of cancer. Changes in DNA methylation patterns, common events in cancer cells, may result in disturbance of the expression of genes that control cell proliferation, differentiation and apoptosis, and lead to carcinogenesis. Furthermore, abnormalities in DNA methylation are promising diagnostic markers for the early detection of cancer (2). In this study, cancer tissues from 101 patients with stage I NSCLC and lung tissues from 30 patients with non-cancerous lung diseases, were detected for the methylation status of 13 genes: PITX2 (paired-like homeodomain 2), RARB (retinoic acid receptor, β), OC2 (one cut homeobox 2), MYF6 (myogenic factor 6), PDGFRA (platelet-derived growth factor receptor, α polypeptide), SOX1 [SRY (sex determining region Y)-box 1], ALX1 (ALX homeobox 1), SIX6 (SIX homeobox 6), PHOX2A (paired-like homeobox 2a), FOXL2 (forkhead box L2), SMPD3 (sphingomyelin phosphodiesterase 3), BCL2 (B-cell CLL/lymphoma 2) and HPP1 (hyperpigmentation, progressive, 1). The methyaltion frequencies of 7 genes: MYF6, SIX6, SOX1, RARB, BCL2, PHOX2A and FOLX2 were significantly higher in stage I NSCLC than in non-cancerous lung diseases.

Materials and methods

Clinical tissue samples

The clinical tissue samples from 101 stage I NSCLC patients and 30 patients with non-cancerous lung diseases used in this study were obtained from the Shanghai Chest Hospital (Shanghai, China) and the First Affiliated Hospital of Guangxi Medical University (Nanning, China). Informed consent was obtained from the patients and the study was approved by the Medical Institutional Review Boards of the two hospitals. Tumor-node-metastasis (TNM) staging/classification of the patients was performed according to the WHO classification. Table I shows the clinical patient profiles.
Table I

Clinical profile of the stage I NSCLC cancer patients and non-cancerous lung diseases controls.

CharacteristicsStage I NSCLC (n=101)Non-cancerous lung lesions (n=30)
Gender
  Female379
  Male6421
Age (years)
  31–4010
  41–5097
  51–603212
  61–70396
  71–80205
  Range32–7942–76
  Median61.28±8.9260.48±7.90
Histological types
  Squamous cell carcinoma24
  Adenocarcinoma48
  Adenosquamous carcinoma14
  Others15
Types of non-cancerous lung lesions
  Pulmonary tuberculosis6
  Bronchiectasis5
  Pulmonary abscess6
  Organizing pneumonia2
  Pulmonary sclerosing hemangioma3
  Pulmonary giant lymph node hyperplasia2
  Pulmonary hamartoma3
  Pulmonary sequestration1
  Pulmonary inflammatory pseudotumor2

NSCLC, non-small cell lung cancer.

DNA isolation and methylation-specific PCR (MSP)

Genomic DNA of clinical tissues were isolated by a standard phenol/chloroform purification method. The primers for MSP were designed according to: http://www.urogene.org/methprimer/index1.html (Table II). The bisulfate conversion and PCR analysis were conducted as previously described (3).
Table II

Primer list for methylation-specific PCR.

Gene nameGenBank No.Sense 5′-3′Antisense 5′-3′Size (bp)
ALX1NC_000012.11TTTTTTGGAGTACGTTATGGAGACAACGCACGTAATACTCGACG116
BCL2NG_009361.1GAAGTCGTCGTCGGTTTGCCCGCACCGAACATC183
FOXL2NG_012454.1GTTATAATATTTTTTCGGTTGTTCGCTAACTCCACGACCTATACTCGAT211
HPP1AF242221AAGAGGGGCGTTAGTTCGCGCTCGCAAACGCTAA158
MYF6NG_021392.1GGAAATGCGTATTCGGTTCCGAACCCCCTAAAATAATCG182
OC2NC_000018.9CGGGTTCGTAGGTGGTTACTCCACGATTTTAAATTCCGA177
PDGFRANG_009250.1CGTCGCGTTGTTTTATTTTCAATCGACCTTACGCCTATCG160
PHOX2ANG_008169.1AGGGATAGTTATAAGCGCGGAAAAATACAAAATCGTATAAACCTCG211
PITX2NG_007120.1GATCGTTAGTCGCGTAGTCGTCCAACTTTCTCGCTCGAT177
RARBNM_016152TCGAGAACGCGAGCGATTCGGACCAATCCAACCGAAACGA146
SIX6NG_008203.1TTAGTAGTTAGGCGTTGGGATCCCTCTCGAAATAATTACTTTACCG150
SMPD3NC_000016.9TCGTAGGATTTTCGAAGGATCCATCACCGACGAATATAATCG160
SOX1NC_000013.10GGTATTGGCGAATTTTAGTGTACAAAAAAACGCTCCCTTAAACG135

Immunohistochemical analysis

Among the 101 cases of stage I NSCLC patients, 92 routinely underwent detection of 8 pathological protein markers by immunohistochemistry before chemotherapy. These pathological markers were: vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (HER-2), p53, p21, epidermal growth factor receptor (EGFR), chromogranin A (CHGA), synaptophysin (SYN) and epithelium membrane antigen (EMA). For each sample, the H&E-stained sections were first reviewed and marked for the selected point. Tumor samples were embedded in paraffin and cut into 3-μm sections. Sections were processed using the Super Sensitive Link-Labeled Detection System (Biogenex, Menarini, Florence, Italy). The first antibodies used in this study were purchased from Sant Cruz Biotechnology (Santa Cruz, CA, USA). The second antibodies were purchased from KangChen Bio-Tech Inc. (Shanghai, China). The enzymatic activity was developed using 3-amino-9-ethylcarbazole (Dako, Milan, Italy) as a chromogenic substrate. The result was scored by conjunction with both staining intensity and the percentage of positive staining cells. Each sample was given an intensity score (0–3) and a percentage of cell positive score (0, <5%; 1, 5–25%; 2, 25–50%; 3, 50–75%; 4, > 75%). An overall immunohistochemistry score was calculated by multiplying the intensity and percentage of cell positive scores. Scores of 1–4 were recorded as +, 6–8 as ++, and 9–12 as +++.

Statistical analysis

All statistical calculations were performed using the SPSS 13.0 software statistical package (SPSS Inc., Chicago, IL, USA). The incidence of hypermethylation in NSCLC tissues vs. the non-cancerous tissues was calculated using a 2×2 Fisher’s exact test. The associations among the pathological variables and the methylation status of the genes were assessed by means of univariate and multivariate logistic-regression analysis. The area under the receiver operating characteristic (ROC) curve (AUC) is a measure of the ability of a continuous marker to accurately classify tumor tissues and non-tumor tissues. Correlations between the expression of pathological markers and gene methylation were examined using the Chi-square test. P<0.05 was considered to indicate a statistically significant result.

Results

Methylation frequencies of the 7 genes differ significantly between stage I NSCLC and non-cancerous controls

Among the 13 genes, the methylation frequencies of 7 genes (MYF6, SIX6, SOX1, RARB, BCL2, PHOX2A and FOLX2) had significant difference between the group of stage I NSCLC and the group of non-cancerous lung diseases (Table III). ROC curves were constructed for each of the 7 genes to classify stage I NSCLC and non-cancerous lung disease. The AUC of the ROC curve for MYF6 was 0.704 (P<0.0001; 95% CI, 0.613–0.795), which was the largest among the 7 genes. The sensitivity and specificity of MYF6 were 64.36 and 93.33%, respectively, in the diagnosis of stage I NSCLC. The AUC of the ROC curves for the other 6 genes (SIX6, SOX1, RARB, BCL2, PHOX2A and FOLX2) ranged from 0.573 to 0.667; the sensitivity of each gene ranged from 29.70 to 51.49% and the specificity ranged from 73.33 to 93.33%, if they were used separately to diagnose stage I NSCLC. The methylation frequencies of the other 6 genes (ALX1, PDGFRA, PITX2, HPP1, OC2 and SMPD3) had no significant difference between tumors and controls, ranging from 24.75 to 59.41% in stage I NSCLC, and from 56.67 to 90.00% in non-cancerous lung diseases (Table III).
Table III

Diagnosis performance of the 13 methylation targets in stage I NSCLC versus non-cancerous lung diseases.

Stage I NSCLC
Non-cancerous lung diseases
TargetSensitivity (%)pos./totalSpecificity (%)pos./totalAUC(95% CI)PPVNPVP-value
MYF664.3665/10193.332/300.7040.613–0.7950.640.93<0.0001
SIX651.4952/10190.003/300.6500.557–0.7430.510.900.0007
SOX150.5051/10173.338/300.5730.475–0.6710.510.700.0213
RARB47.5248/10196.671/300.6670.576–0.7570.480.97<0.0001
BCL237.6238/10190.003/300.6130.515–0.7120.380.900.0035
PHOX2A35.6436/10193.332/300.6240.526–0.7220.360.930.0013
FOLX229.7030/10193.332/300.6100.507–0.7140.300.930.0082
ALX159.4160/10156.6713/30NN0.590.570.1447
PDGFRA37.6238/10170.009/30NN0.380.700.5195
PITX234.6535/10183.335/30NN0.350.830.0724
HPP132.6733/10156.6713/30NN0.330.570.2864
OC224.7525/10186.674/30NN0.250.870.2197
SMPD324.7525/10190.003/30NN0.260.900.0819

NSCLC, non-small cell lung cancer; AUC, area under curve; PPV, positive predictive value; NPV, negative predictive value.

Expression of 8 pathological markers in stage I NSCLC

The positive expression rates of CHGA and SYN were the lowest among the 8 protein (both 3.26%, 3/92), while that of EMA was the highest (100%, 92/92). The positive rates of the other 5 pathological markers were: p21 (8.70%, 8/92), VEGF (28.26%, 26/92), EGFR (29.35%, 27/92), HER-2 (39.13%, 36/92) and p53 (42.39%, 39/92).

Correlation between the methylation status of the 7 genes and the clinical characteristics of NSCLC

The correlations between the methylation status of the 7 genes, individually or combined, with each of the clinical characteristics was primarily assessed by univariate analysis, and the result was displayed in the form of a forest plot (Fig. 1). The methylation status of each of the 7 genes individually had no association with histological types, degree of differentiation or smoking. Next, the correlation between the co-methylation of two genes with the clinical characteristics was assessed in 21 possible pairs of genes. The co-methylation of SIX6 and SOX1 was negatively associated with adenocarcinoma (ADC) with an odds ratio (OR) of 0.24 (95% CI, 0.06–0.90). The co-methylation of BCL2 and RARB was associated with smoking (OR, 9.52; 95% CI, 1.20–75.49). We also analyzed the correlations between co-methylation of three genes with the clinical characteristics. The co-methylation of SIX6, RARB and SOX1 occurred less frequently in adenocarcinoma (ADC) than in squamous cell carcinoma (OR, 0.45; 95% CI, 0.25–0.76). The co-methylation of MYF6, SIX6 and FOLX2 (OR, 2.60; 95% CI, 1.11–6.66) or the co-methylation of SIX6, BCL2 and RARB(OR, 2.37; 95% CI, 1.27–4.44) were associated with smoking (Fig. 1).
Figure 1

Correlation between the methylation status of 7 genes (singly and combined) and the NSCLC clinical characteristics of NSCLC. Univariate logistic-regression analysis was used. L vs. M+H, low vs. medium+high; OR, odds ratio; 95% CI, 95% confidence interval. NSCLC, non-small cell lung cancer.

To verify these correlations, multivariate regression models were established. These indicated that the co-methylation of SIX6 and SOX1, as well as the co-methylation of SIX6, RARB and SOX1, was negatively associated with ADC; the latter association being more significant (SIX6 and SOX1: OR, 0.243; 95% CI, 0.06–0.98; P=0.045; SIX6, RARB and SOX1: OR, 0.008; 95% CI, 0.001–0.149; P=0.007). The association between the co-methylation of SIX6, BCL2 and RARB and smoking has also been validated (OR, 3.09; 95% CI, 1.20–7.95; P=0.019). However, the association beween smoking and the co-methylation of BCL2 and RARB, or the co-methylation of MYF6, SIX6 and FOLX2, had no statistical significance (P>0.05; Table IV).
Table IV

Multivariate analysis of the correlation between gene methylation and clinical characteristics of NSCLC.

CaseGenesStatusNo. of methylationsNo. of casesOR95% CIP-value
ADC/SCCSIX6-SOX1Negative532
Positive19170.240.06–0.980.045
SIX6-RARB-SOX1Negative600
Positive12120.010.001–0.1490.007
SmokingBCL2-RARBNegative851
Positive16159.160.99–84.450.051
SIX6-BCL2-RARBNegative931
Positive873.091.20–7.950.019
MYF6-SIX6-FOLX2Negative871
Positive14138.110.83–78.500.071

NSCLC, non-small cell lung cancer; ADC, adenosquamous carcinoma; SCC, squamous cell carcinoma; OR, odds ratio; CI, confience interval.

A panel of 4 genes for the diagnosis of stage I NSCLC

Combination of several markers is a common strategy to improve diagnostic sensitivity in studies of clinical biomarkers. In this study, the most outstanding gene for the diagnosis of stage I NSCLC, MYF6, was found to be methylated in 65 of the 101 cases of patients with stage I NSCLC, displaying a sensitivity of 64.36%; and the methylation of MYF6 was also found in 2 of the 30 cases of patients with non-cancerous lung diseases, displaying a specificity of 93.3%. In the 36 cases of stage I NSCLC patients without MYF6 methylation, the methylation frequency of SIX6 was 41.67% (15/36), the highest among the 6 genes other than MYF6. Therefore, we made the first combination of MYF6 and SIX6 for the diagnosis of stage I NSCLC. The sensitivity was improved to 79.21%, while the specificity was dropped to 90.00%. However, the AUC of the ROC curve for the combination of MYF6 and SIX6 was 0.774 (P<0.0001; 95% CI, 0.681–0.866), higher than MYF6 alone, which meant that the combination of MYF6 and SIX6 was superior to MYF6 alone in diagnostic power. The methylation of BCL2 was detected in 8 of the 21 cases without methylation of either MYF6 or SIX6; more frequently than the other 4 genes, thus we made the second combination to form a 3-gene panel (MYF6, SIX6 and BCL2). The sensitivity, specificity and AUC were 87.13%, 86.67% and 0.812 (P<0.0001; 95% CI, 0.717–0.906), respectively. Using this method we analyzed a total of 6 panels of genes. The AUC of the 4-gene panel (MYF6, SIX6, BCL2 and RARB) was the largest among the them, and thus made it the best combination of markers in this study. The sensitivity, specificity and AUC of the 4-gene panel were 93.07%, 86.67% and 0.874 (P<0.0001; 95% CI, 0.787–0.960), respectively (Table V).
Table V

Diagnostic performance of different panels of genes in stage I NSCLC, using patients with non-cancerous lung diseases as controls.

NSCLC (n=101)
Non-cancerous lung diseases (n=30)
Sensitivity (%)pos./totalSpecificity (%)pos./totalAUC95% CIPPVNPVP-value
MYF664.3665/10193.332/300.6810.587–0.7740.9700.438<0.0001
MYF6,SIX679.2180/10190.003/300.7740.681–0.8660.9640.571<0.0001
MYF6,SIX6,BCL287.1388/10186.674/300.8120.717–0.9060.9570.667<0.0001
MYF6,SIX6,BCL2,RARB93.0794/10186.674/300.8740.787–0.9600.9610.798<0.0001
MYF6,SIX6,BCL2,RARB, PHOX2A94.0695/10183.335/300.8680.792–0.9640.9500.807<0.0001
MYF6,SIX6,BCL2,RARB, PHOX2A,SOX196.0497/10163.3311/300.8360.731–0.9400.8980.826<0.0003

NSCLC, non-small cell lung cancer; AUC, area under curve; PPV, positive predictive value; NPV, negative predictive value.

Correlations between the expression of pathological markers and gene methylation in stage I NSCLC

We analyzed the data and aimed to explore whether the expression of each protein was associated with the methylation status of any of the 7 genes. We found that the expression of p53 was positively associated with the methylation of BCL2 (P=0.025) and the expression of CHGA was positively associated with the methylation of PHOX2A (P=0.023; Fig. 2 and Table VI).
Figure 2

Analysis of the expression data of pathological markers with the DNA methylation status. (A) Staining patterns of P53 in stage I NSCLC by immunohistochemistry. (B) Sequencing verification of both methylated and unmethylated targeted regions of BCL2. (C) Staining patterns of CHGA in stage I NSCLC by immunohistochemistry. (D) Sequencing verification of both methylated and unmethylated targeted regions of PHOX2A. (E) Methylation status of BCL2 in P53-positive and P53-negative stage I NSCLC patients. (F) Methylation status of PHOX2A in CHGA-positive and CHGA-negative stage I NSCLC patients. NSCLC, non-small cell lung cancer; WT, wide-type; U, unmethylated; M, methylated.

Table VI

Correlations between the expression of pathological markers with gene methylation in stage I NSCLC.

CHGAEGFREMAHER-2P21P53SYNVEGF
MYF6NNNNNNNN
SIX6NNNNNNNN
RARBNNNNNNNN
BCL2NNNNN0.025NN
PHOX2A0.023NNNNNNN
SOX1NNNNNNNN
FOLX2NNNNNNNN

NSCLC, non-small cell lung cancer; N, no sense.

Discussion

This study showed that 7 genes (MYF6, SIX6, SOX1, RARB, BCL2, PHOX2A and FOLX2) were frequently methylated in 101 cases of patients with stage I NSCLC, while rarely methylated in 30 patients with non-cancerous lung diseases. A panel of 4 genes (MYF6, SIX6, BCL2 and RARB) was able to diagnose stage I NSCLC from non-cancerous lung diseases with a sensitivity of 93.07% and a specificity of 86.67%. RARB and BCL2 have already been found to be hypermethylated in NSCLC (4,5). The protein encoded by SOX1 acts as transcription factor and plays a part in the regulation of embryonic development and in the determination of cell fate. Although the methylation of SOX1 have been found to be associated with genitourinary tumors, including cervical cancer, prostate cancer and ovarian cancer, this is the first time that methylation of SOX1 has been found in NSCLC (6–8). It has been reported that SOX1 antibodies are common in the serum of patients with small cell lung carcinoma (SCLC) and may be serve as specific serological markers (9). The methylation of 4 genes, MYF6, SIX6, PHOX2A, FOLX2, has never previously been reported in any types of cancer. MYF6 (12q21) is involved in muscle differentiation, SIX6 (14q23.1) is thought to be involved in eye development, and PHOX2A (11q13.2) is vital for development of the autonomic nervous system (10), FOXL2, as a forkhead transcription factor, may be involved in ovarian development and function. Further studies in the functions of these gene may help to reveal the mechanism of malignant transformation of non-small-cell lung cells. We found that the co-methylation of SIX6 and SOX1 correlated with squamous cell carcinoma (SCC), while the methylation of neither of them individually demonstrated an association with SCC, which may imply that the methylation of these two genes had a superimposed effect on the development of SCC. The co-methylation of BCL2, RARB and SIX6, but not the methylation of either single gene, was associated with smoking. We postulated that cigarette smoking may cause the methylation of the 3 genes through a common pathway. To explore the possible pathway through which gene methylation promotes the carcinogenesis and progression of NSCLC, we investigated the expression of 8 classical components (VEGF, HER-2, P53, P21, EGFR, CHGA, SYN and EMA) of common oncogenic pathways in 92 cases of stage I NSCLC using immuno histochemistry. Analyzing the expression data with the DNA methylation status in this study, we found that the expression of P53 and CHGA were positively associated with the methylation of BCL2 and PHOX2A, respectively. Bcl2, encoded by the gene BCL2, as a critical pro-survival member of Bcl2 protein family which regulates apoptosis, is usually considered to be a downstream target of p53 and can be negatively regulated by p53 (11,12). The overexpression of Bcl2 has been found in numerous types of cancer, including breast cancer, prostate cancer, B-cell lymphoma and colorectal adenocarcinoma (13). However, recently, the hypermethylation of BCL2 gene has been reported in certain types of cancer, such as prostate cancer (14). Furthermore, the methylation of BCL2 was found to be associated with tumor invasion in peripheral pulmonary adenocarcinoma (5). In this study, the methylation frequency of BCL2 was significantly higher in lung tissues of stage I NSCLC, than in non-cancerous lung diseases, and its methylation was positively associated with the expression of P53 in stage I NSCLC. Wang et al reported that wild-type p53 negatively regulated DNMT1 expression through interaction with specificity protein 1 (Sp1) protein (6). Since DNA methylation was usually accompanied by gene silencing, the role of BCL2 in the development and progression of cancer was complicated and further studies are warranted. The methylation of PHOX2A demonstrated positive association with the expression of CHGA. CHGA protein was thought to be a tumor marker in neuroendocrine tumors (NETs), but high expression of CHGA was also found in several other types of solid tumor, such as small cell lung cancer (15), and higher expression of CHGA was associated with higher pathological stage in prostate cancer (16). The function of PHOX2A in carcinogenesis of NSCLC may be correlated with CHGA. Tumorigenesis is an intricate process, involving a variety of genetic and epigenetic aberrations. Even a single tumor-related gene may simultaneously display several types of abnormalities, and contribute to tumorigenesis through several different ways. In this study, 5 genes were for the first time found to be hypermethylated in NSCLC, and the function of those genes and how they act in the carcinogenesis of NSCLC is worth further exploration.
  16 in total

1.  DNA methylation biomarkers for lung cancer.

Authors:  Tibor A Rauch; Zunde Wang; Xiwei Wu; Kemp H Kernstine; Arthur D Riggs; Gerd P Pfeifer
Journal:  Tumour Biol       Date:  2011-12-06

2.  DNA methylation profile during multistage progression of pulmonary adenocarcinomas.

Authors:  Jin-Haeng Chung; Hyun Ju Lee; Baek-Hui Kim; Nam-Yun Cho; Gyeong Hoon Kang
Journal:  Virchows Arch       Date:  2011-04-15       Impact factor: 4.064

3.  SOX antibodies in small-cell lung cancer and Lambert-Eaton myasthenic syndrome: frequency and relation with survival.

Authors:  Maarten J Titulaer; Rinse Klooster; Marko Potman; Lidia Sabater; Francesc Graus; Ingrid M Hegeman; Peter E Thijssen; Paul W Wirtz; Albert Twijnstra; Peter A E Sillevis Smitt; Silvère M van der Maarel; Jan J G M Verschuuren
Journal:  J Clin Oncol       Date:  2009-08-10       Impact factor: 44.544

Review 4.  Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship.

Authors:  Julian R Molina; Ping Yang; Stephen D Cassivi; Steven E Schild; Alex A Adjei
Journal:  Mayo Clin Proc       Date:  2008-05       Impact factor: 7.616

5.  Epigenetic regulation of CpG promoter methylation in invasive prostate cancer cells.

Authors:  Lesley A Mathews; Elaine M Hurt; Xiaohu Zhang; William L Farrar
Journal:  Mol Cancer       Date:  2010-10-07       Impact factor: 27.401

6.  The expression of PHOX2A, PHOX2B and of their target gene dopamine-beta-hydroxylase (DbetaH) is not modified by exposure to extremely-low-frequency electromagnetic field (ELF-EMF) in a human neuronal model.

Authors:  Roberta Benfante; Ruth Adele Antonini; Niels Kuster; Juergen Schuderer; Christian Maercker; Franz Adlkofer; Francesco Clementi; Diego Fornasari
Journal:  Toxicol In Vitro       Date:  2008-05-17       Impact factor: 3.500

7.  A novel set of DNA methylation markers in urine sediments for sensitive/specific detection of bladder cancer.

Authors:  Jian Yu; Tongyu Zhu; Zhirou Wang; Hongyu Zhang; Ziliang Qian; Huili Xu; Baomei Gao; Wei Wang; Lianping Gu; Jun Meng; Jina Wang; Xu Feng; Yixue Li; Xuebiao Yao; Jingde Zhu
Journal:  Clin Cancer Res       Date:  2007-12-15       Impact factor: 12.531

Review 8.  The BCL2-family of protein ligands as cancer drugs: the next generation of therapeutics.

Authors:  WenJing Liu; Anca Bulgaru; Missak Haigentz; C A Stein; Roman Perez-Soler; Sridhar Mani
Journal:  Curr Med Chem Anticancer Agents       Date:  2003-05

9.  An epigenetic marker panel for screening and prognostic prediction of ovarian cancer.

Authors:  Her-Young Su; Hung-Cheng Lai; Ya-Wen Lin; Yu-Ching Chou; Chin-Yu Liu; Mu-Hsien Yu
Journal:  Int J Cancer       Date:  2009-01-15       Impact factor: 7.396

10.  Absolute quantitation of DNA methylation of 28 candidate genes in prostate cancer using pyrosequencing.

Authors:  Nataša Vasiljević; Keqiang Wu; Adam R Brentnall; Dae Cheol Kim; Mangesh A Thorat; Sakunthala C Kudahetti; Xueying Mao; Liyan Xue; Yongwei Yu; Greg L Shaw; Luis Beltran; Yong-Jie Lu; Daniel M Berney; Jack Cuzick; Attila T Lorincz
Journal:  Dis Markers       Date:  2011       Impact factor: 3.434

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Journal:  Oncol Lett       Date:  2016-07-05       Impact factor: 2.967

Review 2.  Research status and funding trends of lung cancer biomarkers.

Authors:  Cui Li; Wei Hong
Journal:  J Thorac Dis       Date:  2013-10       Impact factor: 2.895

3.  Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis.

Authors:  Kathleen Conway; Sharon N Edmiston; Joel S Parker; Pei Fen Kuan; Yi-Hsuan Tsai; Pamela A Groben; Daniel C Zedek; Glynis A Scott; Eloise A Parrish; Honglin Hao; Michelle V Pearlstein; Jill S Frank; Craig C Carson; Matthew D Wilkerson; Xiaobei Zhao; Nathaniel A Slater; Stergios J Moschos; David W Ollila; Nancy E Thomas
Journal:  J Invest Dermatol       Date:  2018-12-06       Impact factor: 8.551

4.  Investigation the Role of Autophagy in Non-Small Cell Lung Cancer.

Authors:  Minoo Pargol; Shima Zare Karizi; Masoumeh Akbari; Bahareh Nourmohammadi; Mohammad Behgam Shadmehr; Morteza Karimipoor; Shohreh Zare Karizi
Journal:  Asian Pac J Cancer Prev       Date:  2021-03-01

5.  Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC).

Authors:  Shicheng Guo; Fengyang Yan; Jibin Xu; Yang Bao; Ji Zhu; Xiaotian Wang; Junjie Wu; Yi Li; Weilin Pu; Yan Liu; Zhengwen Jiang; Yanyun Ma; Xiaofeng Chen; Momiao Xiong; Li Jin; Jiucun Wang
Journal:  Clin Epigenetics       Date:  2015-01-22       Impact factor: 6.551

6.  An exceptional case of myelodysplastic syndrome with myelofibrosis following combination chemotherapy for squamous cell lung cancer.

Authors:  Yi-Hao Wang; Rong Fu; Zong-Hong Shao
Journal:  Cancer Biol Med       Date:  2013-06       Impact factor: 4.248

Review 7.  The expression profile and clinic significance of the SIX family in non-small cell lung cancer.

Authors:  Qian Liu; Anping Li; Yijun Tian; Yu Liu; Tengfei Li; Cuntai Zhang; Jennifer D Wu; Xinwei Han; Kongming Wu
Journal:  J Hematol Oncol       Date:  2016-11-08       Impact factor: 17.388

8.  Role of p14ARF and p15INK4B promoter methylation in patients with lung cancer: a systematic meta-analysis.

Authors:  Xinmei Yang; Lei Yang; Wanrong Dai; Bo Ye
Journal:  Onco Targets Ther       Date:  2016-11-11       Impact factor: 4.147

9.  Systems biology approach to stage-wise characterization of epigenetic genes in lung adenocarcinoma.

Authors:  Meeta P Pradhan; Akshay Desai; Mathew J Palakal
Journal:  BMC Syst Biol       Date:  2013-12-26

10.  Expression of a SOX1 overlapping transcript in neural differentiation and cancer models.

Authors:  Azaz Ahmad; Stephanie Strohbuecker; Cristina Tufarelli; Virginie Sottile
Journal:  Cell Mol Life Sci       Date:  2017-07-03       Impact factor: 9.261

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